Over the last years, our lifestyle has been positively upset by the sudden advent of technology. The Internet of Things (IoT), offering universal and ubiquitous connectivity to both people and objects, revealed to be the silver bullet for enabling a vast number of previously unexpected applications. In particular, media streaming providers are growing in business and scope, and we can forecast that soon, video streaming will substitute TV broadcasting activities. With the increasing success of multi-user smart environments, empowered by new-generation smart devices and IoT architectures, multimedia contents (i.e., images and videos) need to be effectively accessed anytime and anywhere. Recent advances in computer vision technologies have made the development of intelligent monitoring systems for video surveillance and ambient-assisted living. Such a scenario permits better integration among technologies, multimedia content, and end-users. However, there are several challenges, and some are still open. More precisely, due to the sensitivity of some multimedia content (e.g., video-surveillance streams), it is paramount to preserve users’ privacy. Again, it is necessary to guarantee the integrity of usage rights during any multimedia transmission process, starting from the video encoding phase. In this way, the private content is disclosed only when the stream is decoded on the other endpoint, by the legitimate user. In this article, we present a secure video transmission strategy that can address the challenges mentioned above. The proposed strategy takes advantage of both watermarking and video scrambling techniques to make it possible for the secure and privacy-preserving transmission of multimedia streaming. Through our proposal, multimedia streaming is of low quality and thus unusable. However, it can be fully recovered and enjoyed only by authorized users. Finally, due to its low complexity and energy-efficiency, our proposal is particularly suitable for onboard implementations.

By calculating the centrality measures of the nodes of the SARS-CoV-2 protein interactome network, we have identified the viral proteins of potential greatest interest for further experimental investigation to understand the mechanisms by which SARS-CoV-2 attacks cells and to identify possible therapeutic targets. The proteins identified in this study including NSP13, NSP7, ORF3a, ORF8a, and ORF8b, were found to be involved in crucial processes of the viral life cycle, and some of them are currently suspected to be antiviral targets. These results thus demonstrate the importance - and the predictive power- of the in silico analysis of the viral interactome to guide and support experimental investigation, which could otherwise be too complex and time-consuming to carry out in clinical and experimental research, given the size and interaction density of the viral protein network and the current still partial knowledge of this new virus.

Abstract Nowadays, several tools have been proposed to support the operations performed during a security assessment process. In particular, it is a common practice to rely on automated tools to carry out some phases of this process in an automatic or semiautomatic way. In this article, we focus on tools for the automatic generation of custom executable payloads. Then, we will show how these tools can be transformed, through some human‐oriented modifications on the generated payloads, into threats for a given asset's security. The danger of such threats lies in the fact that they may not be detected by common antivirus (AVs). More precisely, in this article, we show a general approach to make a payload generated through automated tools run undetected by most AVs. In detail, we first analyze and explain most of the methods used by AVs to recognize malicious payloads and, for each one of them, we outline the relative strengths and flaws, showing how these flaws could be exploited using a general approach to evade AVs controls, by performing simple human‐oriented operations on the payloads. The testing activity we performed shows that our proposal is helpful in evading virtually all the most popular AVs on the market. Therefore, low‐skilled malicious users could easily use our approach.

Summary At present, 96% of the resources available into the World‐Wide‐Web belongs to the Deep Web, which is composed of contents that are not indexed by search engines. The Dark Web is a subset of the Deep Web, which is currently the favorite place for hiding illegal markets and contents. The most important tool that can be used to access the Dark Web is the Tor Browser. In this article, we propose a bottom‐up formal investigation methodology for the Tor Browser's memory forensics. Based on a bottom‐up logical approach, our methodology enables us to obtain information according to a level of abstraction that is gradually higher, to characterize semantically relevant actions carried out by the Tor browser. Again, we show how the proposed three‐layer methodology can be realized through open‐source tools. Also, we show how the extracted information can be used as input to a novel Artificial Intelligence‐based architecture for mining effective signatures capable of representing malicious activities in the Tor network. Finally, to assess the effectiveness of the proposed methodology, we defined three test cases that simulate widespread real‐life scenarios and discuss the obtained results. To the best of our knowledge, this is the first work that deals with the forensic analysis of the Tor Browser in a live system, in a formal and structured way.

Efficient HPC libraries often expose multiple tunable parameters, algorithmic implementations, or a combination of them, to provide optimized routines. The optimal parameters and algorithmic choices may depend on input properties such as the shapes of the matrices involved in the operation. Traditionally, these parameters are manually tuned or set by auto-tuners. In emerging applications such as deep learning, this approach is not effective across the wide range of inputs and architectures used in practice. In this work, we analyze different machine learning techniques and predictive models to accelerate the convolution operator and GEMM. Moreover, we address the problem of dataset generation, and we study the performance, accuracy, and generalization ability of the models. Our insights allow us to improve the performance of computationally expensive deep learning primitives on high-end GPUs as well as low-power embedded GPU architectures on three different libraries. Experimental results show significant improvement in the target applications from 50% up to 300% compared to auto-tuned and high-optimized vendor-based heuristics by using simple decision tree- and MLP-based models.

Summary Conventional privacy‐enforcement mechanisms, such as encryption‐based ones, are frequently used to prevent third‐party eavesdroppers to intercept confidential information exchanged between two or more parties. However, the use of such mechanisms can be perceivable and it alerts the involved intercepting entities that could devote some effort in trying to remove the protection, eg, by cracking the encryption keys used or by exploiting the vulnerabilities of the technological solution used to protect the data. Sometimes, from the security point of view, avoiding to draw the attention or suspect to intermediate intercepting entities, may be better than protecting a data in a conventional manner. In such direction, one of the most effective approaches is hiding the secret information to be exchanged inside other data, through steganographic techniques. In this work, we exploit, for this specific purpose, the hierarchical structure of a compressed archive, as well as the algorithms and parameters used to create and maintain such archive. It is important to point out that, by doing this, the secret information is in no way semantically related to the contents of the compressed archive. This can be extremely useful in many cloud‐based situations where several confidential data is moved across multiple independent data center, which are under the control of different and not always fully trusted authorities. The effectiveness of this proposal has been assessed by using a properly designed and implemented prototype, where extensive tests have been performed within the context of a proof‐of‐concept.

We live in a digital era in which communication is largely based on the exchange of digital information on data networks. Communication is often pictured as a sender that transmits a digital file to a receiver. This file travels from a source to a destination and, to have a quick and immediate communication, we need an encoding strategy that should be efficient and easy yet secure. This communication could be based on a layout articulated in two operations that are heterogeneous and in some case conflicting but that are needed to be applied to the original file to have efficiency and security. These two operations are data compression and encryption. The aim of this work is to study the combination of compression and encryption techniques in digital documents. In this paper we will test the combinations of some of the state-of-the-art compression and cryptography techniques in various kinds of digital data.

Functional magnetic resonance imaging (fMRI) provides an effective and noninvasive tool for researchers to understand cerebral functions and correlate them with brain activities. In addition, with the ever-increasing diffusion of the Internet, such images may be exchanged in several ways, allowing new research and medical services. On the other hand, ensuring the security of exchanged fMRI data becomes a main concern due to their special characteristics arising from strict ethics and legislative and diagnostic implications. Again, the risks increase when dealing with open environments like the Internet. For this reason, security mechanisms that ensure protection of such data are strongly required. However, we remark that the mechanisms commonly employed for data protection are doomed to fail when dealing with imaging data. In this article, we propose a novel watermarking scheme explicitly addressed for this type of imaging. Such a scheme can be used for several purposes, particularly to ensure authenticity and integrity. Moreover, we show how to integrate our scheme within commercial off-the-shelf fMRI system. Finally, the validity and the efficiency of our scheme has been assessed through testing.

We present a new low-complexity algorithm for hyperspectral image compression that uses linear prediction in the spectral domain. We introduce a simple heuristic to estimate the performance of the linear predictor from a pixel spatial context and a context modeling mechanism with one-band look-ahead capability, which improves the overall compression with marginal usage of additional memory. The proposed method is suitable to spacecraft on-board implementation, where limited hardware and low power consumption are key requirements. Finally, we present a least-squares optimized linear prediction technique that achieves better compression on data cubes acquired by the NASA JPL Airborne Visible/Infrared Imaging Spectrometer (AVIRIS).

In previous work we considered LPVQ, a compression algorithm based on locally optimal partitioned vector quantization that can be used to compress hyperspectral images by applying partitioned VQ to the spectral signatures (e.g., to the 224 16-bit values of a NASA AVIRIS pixel) and then encoding error information with a threshold that can be varied from high quality lossy to near lossless to lossless (e.g., 50-to-1 lossy, 10-to-1 near lossless, or 3-to-1 lossless). An advantage of LPVQ is extremely fast decoding (table lookup followed by entropy decoding), but it is at the cost of more complex encoding. Here we present a new low complexity algorithm for hyperspectral image compression, called SLSQ, that employs linear prediction targeted at spectral correlation followed by entropy coding of the prediction error. We then consider how SLSQ can be combined with LPVQ in a scenario commonly arising in practice. In this scenario, a low-complexity lossless encoder on the remote acquisition platform compresses the data for transmission to a central computing facility, where it is processed and re-coded using LPVQ, so that the compressed data can be distributed to the final users at various quality levels. The VQ indices of the LPVQ form a lossy compressed image of only about 2% of the original size; this small image can be employed to greatly reduce the time for browsing and classification.

We address the problem of improving the scene cut quality in fixed bit-rate real-time video decoding such as is used in the H.263 and MPEG standards. In low bandwidth applications, scene cuts can cause the bits required to encode a single frame to greatly exceed the target average bits per frame, and necessitate the skipping of other frames to provide sufficient time to transmit the scene cut frame. We present an optimal algorithm for minimizing the number of skipped frames and keep the decoding synchronized. Although the algorithm requires additional encoding complexity, there is no change in decoding complexity (in fact, no change to the decoder at all). Experimental results, obtained with a simplified strategy within the framework of H.263+ video encoding, confirm that the method provides an effective alternative to current frame skipping strategies. The overall quality in the presence of scene cuts is improved with respect the TMN-8 rate control. Although the overall bit rate benefits from our method, our focus is to improve the quality of the video where scene cuts occur (by reducing skipped frames and improving decoder synchronization). The approach here can be combined with more sophisticated rate controls, as, for example, the newer rate-distortion optimized TMN-10 and TMN-11.

In this paper, we survey some of the recent advances in lossless compression of continuous-tone images. The modeling paradigms underlying the state-of-the-art algorithms, and the principles guiding their design, are discussed in a unified manner. The algorithms are described and experimentally compared.

In past years, there have been several improvements in lossless image compression. All the recently proposed state-of-the-art lossless image compressors can be roughly divided into two categories: single and double-pass compressors. Linear prediction is rarely used in the first category, while TMW, a state-of-the-art double-pass image compressor, relies on linear prediction for its performance. We propose a single-pass adaptive algorithm that uses context classification and multiple linear predictors, locally optimized on a pixel-by-pixel basis. Locality is also exploited in the entropy coding of the prediction error. The results we obtained on a test set of several standard images are encouraging. On the average, our ALPC obtains a compression ratio comparable to CALIC while improving on some images.

The practical lossless digital image compressors that achieve the best results in terms of compression ratio are also simple and fast algorithms with low complexity both in terms of memory usage and running time. Surprisingly, the compression ratio achieved by these systems cannot be substantially improved even by using image-by-image optimization techniques or more sophisticate and complex algorithms. Meyer and Tischer (1998) were able, with their TMW, to improve some current best results (they do not report results for all test images) by using global optimization techniques and multiple blended linear predictors. Our investigation is directed to determine the effectiveness of an algorithm that uses multiple adaptive linear predictors, locally optimized on a pixel-by-pixel basis. The results we obtained on a test set of nine standard images are encouraging, where we improve over CALIC on some images.

Summary form only given. Constantinescu and Storer (1994) introduced an adaptive vector quantization algorithm (AVQ) that combines adaptive dictionary techniques with vector quantization (VQ). The algorithm typically equals or exceeds the compression of the JPEG standard on different classes of images and it often outperforms traditional trained VQ. We show how it is possible to improve AVQ on the class of images on which JPEG does best (i.e., "magazine photographs"). The improvement is possible by exploring the similarities in the dictionary built by AVQ. This is achieved by transforming the input vectors in a way similar to the one used in mean-shape-gain VQ (Oehler and Gray, 1993). In MSGVQ each vector x~/spl isin/R/sup n/ is decomposed as x~=g/spl middot/s~+E/sub x//spl middot/1~, where g=/spl par/x~-E/sub x//spl middot/1~/spl par/ and s~=(x~-E/sub x//spl middot/1~)/g; mean, gain and shapeare quantized separately. We apply this idea to AVQ, changing the match heuristic: letandbe respectively theof the dictionary block b and of the one anchored in p. The entry b is the best match if d(x~/sub p/,x/spl circ/)/spl les/T (x/spl circ/=g/sub p//spl middot/s~/sub b/+E/sub p//spl middot/1~) and its size is maximum. The tripleis entropy coded and sent to the decoder. This simple modification of the match heuristic allows AVQ to improve the compression ratio on many images. In some cases this improvement is as high as 60%. Along with the better compression results, there is also an improvement in the overall visual quality of the decoded image, especially at high compression rate.

Constantinescu and Storer (1994) introduced a single-pass vector quantization algorithm that with no specific training or prior knowledge of the data was able to achieve better compression results with respect to the JPEG standard, along with a number of computational advances such as: adjustable fidelity/compression tradeoff, precise guarantees on any l/spl times/l sub-block of the image, and fast table-lookup decoding. We improve that basic algorithm by blending it with the mean shape-gain vector quantization (MSGVQ) compression scheme. This blending allows a slightly better performance in terms of compression and a clear improvement in visual quality.

We present a new trellis coded vector residual quantizer (TCVRQ) that combines trellis coding and vector residual quantization. We propose new methods for computing quantization levels and experimentally analyze the performances of our TCVRQ in the case of still image coding. Experimental comparisons show that our quantizer performs better than the standard tree and exhaustive search quantizers based on the generalized Lloyd algorithm (GLA).

Motion Compensation is one of the most effective techniques used in interframe data compression. In this paper we present a parallel block-matching algorithm for estimating interframe displacement of blocks with minimum error. The algorithm is designed for a simple parallel architecture to process video in real time. The blocks may have variable size and shape depending on a split-and-merge technique. The algorithm performs a segmentation of the image into regions (objects) moving in the same direction and uses this knowledge to improve the transmission of the displacement vectors. This segmentation identifies the part of the frame "active" with respect to the previous frame and preserves some of the spatial correlation between blocks.>

Motion compensation is one of the most effective techniques used in interframe data compression. The authors present a parallel block-matching algorithm for estimating interframe displacement of small blocks with minimum error. The algorithm is designed for a grid architecture to process video in real time. The blocks may have variable size depending on a split-and-merge technique. The algorithm performs a segmentation of the image into regions (objects) moving in the same direction and uses this knowledge to improve the transmission of the displacement vectors.>